本文简要介绍python语言中 sklearn.metrics.plot_roc_curve 的用法。 用法: sklearn.metrics.plot_roc_curve(estimator, X, y, *, sample_weight=None, drop_intermediate=True, response_method='auto', name=None, ax=None, pos_label=None, **kwargs) 已弃用:函数 plot_roc_curve 在1.0 中已弃用,并...
3.sklearn中roc曲线 1fromsklearn.metricsimportroc_curve 2tpr,fpr,thresholds=roc_curve(y_test,y_pred) 3 4importmatplotlib.pyplotasplt 5plt.plot(fpr,tpr) 6plt.xlim([0.0,1.0]) 7plt.ylim([0.0,1.0]) 8plt.title('ROC curve for diabetes classifier') 9plt.xlabel('False Positive Rate (1 -...
代码示例 importnumpyasnpfromsklearn.metricsimportroc_curve y_test=np.array([1,1,0,1,1])y_score=np.array([0.1,0.3,0.35,0.6,0.8])fpr,tpr,thresholds=roc_curve(y_test,y_score)(fpr,tpr,thresholds)# (array([0., 0., 0., 1., 1.]),# array([0. , 0.25, 0.5 , 0.5 , 1. ]),...
fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2) #得到fpr,tpr, thresholds 返回值对应如下: 得到一组fpr和tpr之后即可画出该次测试对应的roc曲线 plt.plot(fpr,tpr,marker = 'o') plt.show() 得到ROC曲线: fig.4.ROC曲线 求出AUC: from sklearn.metrics import auc AUC = auc...
ROC曲线就由这两个值绘制而成。接下来进入sklearn.metrics.roc_curve实战,找遍了网络也没找到像我一样解释这么清楚的。 import numpy as np from sklearn import metrics y = np.array([1, 1, 2, 2]) scores = np.array([0.1, 0.4, 0.35, 0.8]) ...
#导入plot_roc_curve,roc_curve和roc_auc_score模块 from sklearn.metrics import plot_roc_curve,roc_curve,auc,roc_auc_score #导入三个不同的分类器:LogisticRegression,DecisionTree和KNN from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier ...
基于python绘制ROC曲线,直接附代码: fromsklearn.metrics importroc_curve,aucfromsklearn...(index) fpr_val = fpr(index) ## 绘制roc曲线图plt.subplots(figsize=(7,5.5)); plt.plot(fpr, tpr, color 评估指标:精确率,召回率,F1_score,ROC,AUC ...
from sklearn.metrics import plot_roc_curve,roc_curve,auc,roc_auc_score #导入三个不同的分类器:LogisticRegression,DecisionTree和KNN from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier ...
from sklearn.metrics importprecision_recall_curveprecisions,recalls,thresholds =precision_recall_curve(y_train_3,y_scores) def plot_precision_recall_vs_threshold(precisions, recalls, thresholds): plt.plot(thresholds, precisions[:-1], "b--", label="Precision", linewidth=2) ...
有 [None, ‘micro’, ‘macro’ (default), ‘samples’, ‘weighted’]⼏种。max_fpr：设置最⼤的fpr，取None即可。 返回值：float类型的auc值，即ROC曲线下的⾯积。 2、sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)